A data analyst uses programming tools to extract large amounts of complex data and finds relevant information from that data. In short, an analyst is someone who extracts meaning from messy data. An analyst using Python for data analysis needs to have skills in the following areas in order to be valued: area domain To be able to extract data and obtain data relevant to their workplace, an analyst needs to have knowledge of their surroundings. Programming Skills As a data analyst, you will need to know the right libraries to use in order to clean the data, filter it, and get results from it.
Statistic An analyst may need to use some statistical tools to assist in extracting the data Data Visualization Skills with Python A data analyst needs to have great skills in data visualization in order to summarize and present data to third Estonia Phone Number parties. Result Finally, an analyst needs to communicate their findings to a stakeholder or customer. This means that they will need to report the history of the data, and have the ability to narrate it. In this article, I bring you a complete process of using Python for data analysis. If you follow this tutorial and code everything like I did, you can then use these codes and tools for future data analysis projects.
What we will see in this article The Power of the Python Professional for Data Analysis prerequisites The analysis Data reading Pandas Specifications Data visualization data return We'll start with downloading and cleaning the dataset, and then moving on to analysis and visualization. Finally, we'll tell a story around our findings from this data. I'll be using a Kaggle dataset called the Pima Indian Diabetes Database , which you can download to perform the analysis. Prerequisites For this entire review, I'll be using the
Notebook . You can use any Python IDE you like.